Visualizing feature importances
Your random forest classifier from earlier exercises has been fit to the telco
data and is available to you as clf
. Let's visualize the feature importances and get a sense for what the drivers of churn are, using matplotlib
's barh
to create a horizontal bar plot of feature importances.
This exercise is part of the course
Marketing Analytics: Predicting Customer Churn in Python
Exercise instructions
- Calculate the feature importances of
clf
. - Use
plt.barh()
to create a horizontal bar plot ofimportances
.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Calculate feature importances
importances = ____.____
# Create plot
____.____(range(X.shape[1]), ____)
plt.show()